Abstract

The work presented in this paper analyses the viability of using a cell-phone as students' guidance for literature selection. The integrated cell-phone camera is used to recognize the book covers in the bookstores and libraries. The chosen solution is based on client-server architecture and the object recognition is based on local features. Detecting, identifying, and recognizing salient regions or feature points in images is a very important and fundamental problem to the artificial intelligence and computer vision community. This paper mainly focuses on the comparison, in terms of time and performance, of two promising new approaches for markerless object recognition algorithms: the Scale-Invariant Feature Transform (SIFT) and the Speeded Up Robust Features (SURF). The study was performed using a smart cell-phone with Symbian OS and the results are reported.